Approximately 75% of adults over the age of 65 years are affected by two or more chronic medical conditions. Our proposed methodological research specifically focuses on methods to develop individualized Absolute Risk calculators for competing patient-centered outcomes (PCO) (i.e. outcomes deemed important by patients) and patient reported outcomes (PRO) (i.e. outcomes patients report instead of physiologic test results), and includes a methodology toolkit and dissemination via demonstration web application. We posit that the heterogeneity of treatment effect on patients with multiple chronic conditions likely depends upon the patients' individual characteristics and coexisting conditions. The absolute risk of an outcome is the probability that a person receiving a given treatment will experience that outcome within a pre-defined interval of time, during which they are simultaneously at risk for other competing outcomes. This allows for determination of likelihood of a given outcomes with and without a treatment. Our Specific Aims are to: 1) Develop a methodology to create an individualized absolute risk calculator for competing outcomes. We will use propensity score matching to strengthen causal inference of specific treatments. Patient characteristics and MCC will be explanatory factors for the heterogeneity of PCO and PRO. 2) Create a methodology toolkit that facilitates application of this methodology including dissemination via web applications to a wide range of health conditions, datasets and future research. 3) We will illustrate the methodology with chronic obstructive pulmonary disease (COPD), as persons with COPD usually have multiple chronic conditions, and COPD has several recommended treatments. 4) Create a demonstration web application for COPD using the individualized absolute risk calculator for occurrence of competing PCO and PRO within a 1-year interval. COPD is an ideal condition to develop these individualized risk methods, because it is common, is often associated with other health conditions, and has several recommended treatments. We will use de-identified nationally representative data collected by Agency for Healthcare Research and Quality. Anticipated Impact: We propose to develop innovative method that will have wide-spread application regarding individualized absolute risk calculations for competing outcomes. There are many patients with multiple chronic conditions who will face complex treatment decision making wherein treatment effects may vary by personal characteristics. Innovations in the proposal include active exploration of the timing of the outcomes and the key concept that the occurrence of one outcome does not necessarily eliminate the possibility of the patient having a second outcome. Also, by using a technique called propensity scoring, which makes comparable groups, we will incur less bias than existing methodology. We will disseminate these innovative methods to many researchers and health care professionals, by creating a toolkit and web application.